Bipolar disorder and schizophrenia are highly heritable disorders that are likely due to the actions of a large number of genes. Genome-wide association studies (GWAS) in humans have identified some key candidate genes, but together they explain only a fraction of the heritability. One of the strongest and best replicated candidat genes is CACNA1C, which has been shown to significantly contribute to disease risk for bipolar disorder, schizophrenia, and major depression. Genetic interactions are important in determining a number of traits in model organisms, and may also account for missing heritability in human psychiatric disease. Despite this promise, little progress has been made in understanding epistasis due to the statistical burden of testing for all pairwise interactions across the genome. The key long-term goal of this project is to use mouse genetics to identify epistatic modifier genes that influence risk for psychiatric disease in humans. We will use the Cacna1c+/- (heterozygous knockout) mouse, which exhibits key phenotypes involved in several psychiatric diseases. We will create a panel of isogenic F1 (first filial generation) offspring tha express the Cacna1c+/- allele on a variety of inbred backgrounds. As the phenotypic effects of transgenes are well known to differ across genetic backgrounds, we will take advantage of the phenotypic differences in Cacna1c+/- mice of different backgrounds to map the genetic variants that underlie them. All F1s will be isogenic for half their genome on which they inherit the Cacna1c +/- or +/+ allele on a B6 background; the other half will be of a variable inbred strain. I Specific Aim 1 we will generate this panel of F1 offspring harboring the Cacna1c mutant or wild-type allele on a variety of inbred backgrounds. We will phenotype the panel of F1 mice on a battery of behavioral tests that model specific aspects of neuropsychiatric disease relevant to bipolar disorder and schizophrenia and are already known to be altered in Cacna1c+/- mice. In Specific Aim 2 we will use this phenotypic data to conduct a GWAS using a statistical model that we developed for this study. Aside from genotype at the Cacna1c allele, genotype data for each F1 will be available from public databases. Our GWAS analysis will reveal markers that are associated with the phenotypic traits themselves, similar to results of a conventional GWAS study, as well as markers that modify the susceptibility or resilience to the effect of Cacna1c observed in some but not other F1s. We will then prioritize and narrow down candidate loci using bioinformatics resources; validate candidates using gene expression assays from brains of F1 mice; and conduct final validation by testing in human genetic datasets to which we will have access by collaboration. This is the first large-scale, efficient approach to mapping modifier loci. Findings of this work will be translated directly to human datasets where they may prove to explain individual risk to psychiatric disorder and generate new biological insights and treatment options for psychiatric disease.